These results demonstrate the utility of the explanations when they are provided to a naive CTDL agent. However, because the explanations are a simple list of states, values and actions, they can be used by any RL algorithm. In order to demonstrate this, Figure REF  in the Supplementary Materials shows the performance of Advantage Actor-Critic (A2C) [1]} both with and without an explanation from CTDL. As with the naive CTDL agents, the average reward was higher and the policy appeared to be more robust when the naive A2C agent received an explanation at the start of training. This illustrates that our approach is algorithm-independent and therefore highly flexible.
